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This paper presents an audio-visual speech recognition framework based on articulatory features, which tries to combine the advantages of both areas, and shows a better recognition accuracy compared to a phone-based recognizer. In one's approach, the paper uses HMMs to model abstract articulatory classes, which are extracted in parallel from both the speech signal and the video frames. The N-best outputs of these independent classifiers are combined to decide on the best articulatory feature tuples. By mapping these tuples to phones, a phone stream can be generated. A lexical search finally maps this phone stream to meaningful word transcriptions. The paper demonstrates the potential of the approach by a preliminary experiment on the GRID database, which contains continuous English voice commands for a small vocabulary task.

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Format:PDFSize:182 KB
Date:Jun 2007
Pages:5

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